Metal Mickey wants your job!

An interesting – worrying? – story this week about robots replacing humans. Foxconn, a Chinese company which makes products for Apple and Samsung (and possibly others) has replaced 60,000 workers with robots. Which probably isn’t great news, economically, if you work on a production line.

There’s a growing amount of speculation that the age of the robot may soon be upon us – here I cite in evidence the BBCs Will a robot take your job? article last year. But surely universities are safe from this kind of thing?

Not coming to a University near you

Don’t be so sure. Walking, talking, robots probably aren’t going to be lecturing any time soon, but a different sort of robot is having an impact. Georgia State University in the US of A has started using a chatbot – a computer programme which independently ‘talks’ to users via text – to handle queries from incoming students. Claiming to answer more than 99% of student queries, the chatbot, called Pounce, has been helping more than 3,000 students apply for financial aid; apply for accommodation and enrol in courses. In its first month of operation.

This is clearly a big thing. If the communication channels for conversion, admission and enrolment can be augmented by such technology, many universities will want to replicate this, and not only across the Atlantic. Speed and quality of response matter in converting an applicant to a student. And this directly impacts on universities’ success, so we can expect to see this grow. And if you have Pounce you don’t need so many people on the phones or at the help desks.

Pounce is a “product of artificial intelligence and supervised machine learning” according to the blurb from AdmitHub, its vendor. Its answering questions which cover a range of standard issues, but can come from left field:

everything from “When will I get my scholarship package?” to “Can my dog live in the dorm with me?”, according to the blurb. 

There are lots of other university situations where this sort of transaction happens. Exam time, submission of coursework; library and IT rules, graduation ceremonies. Universities are guiding students through all sorts of procedures, and a capacity to answer questions naturally, quickly, reliably and trackably, any time of day or night, is bound to be appealing to university managers. Where admission goes, other services will follow.

So it seems that, for some professional service staff at least, Metal Mickey might not want your job. But Hal from 2001 - A Space Odyssey might.

A framework for service quality - second draft

In July I posted about the framework for service quality which I'm working on. The post has generated a lot of interest (I can tell that from the Google Analytics) and some very thoughtful feedback.

I'm sharing now the second draft of the elements - you can download it from my Resources page as a pdf, and the pdf itself is here. In this document I ask a specific question for feedback - in relation to staff appraisals - and say a bit more about the concept of a maturity matrix. You can also see how the draft has changed from the first version, and again, comments and feedback are welcomed.

The point of the exercise is to create a diagnostic tool which anyone can use to help them measure and improve the quality of a professional service in higher education. It's published with a creative commons license - you're free to use and amend it as you wish, as long as you acknowledge the source, and share what you've done on the same basis.

You can post comments in reply to this blog; or reply to the tweets or linked-in posts which I use to publicise it. Or, if you'd rather, email me directly - hugh @ hughjonesconsulting.co.uk.

Thank you!

Improving Service Quality

I'm working on a tool to help universities measure and improve the quality of their professional services.

My idea is to develop a maturity matrix (see here for an example of one relating to quality management), which shows the different elements of service quality, and the different stages of development. It's an approach which is familiar in IT, and many thanks to my various IT colleagues who have helped me to learn about the approach.

As a first step, I'm hoping to articulate the different elements which make up service quality - the Y axis of the matrix. I've written a first draft, which is available here, and on which I'd really appreciate any comments or feedback which people may have.

My aim is to create a framework which anyone can use, so it will need to be on some sort of creative commons license. I'm not asserting copyright on the draft, but I am saying that if anyone uses it or subsequent drafts, please share it on the same terms and attribute the source. I will want to use the framework commercially (I have to earn a living!), and I wouldn't want to rule out anyone else doing so, but equally the more feedback there is, the better it will be for all.

Please let me know what you think of the draft. Full acknowledgement will be given to those who help develop it, on the understanding that your comments are given freely and without copyright. If you'd be interested in working more closely with me in developing this framework, I'd love to hear from you.

You can post comments on the draft elements as a reply to this blog post (and it would be great if you did, as then a conversation could start!), or email me: hugh @ hughjonesconsulting.co.uk.

Thank you!

Big data, small budgets - 7 ways to make a difference

You don't need a big budget to get more from the data you have.

I saw this morning an advert from a company specialising in big data for universities – how to join together the data that universities already hold, turn it into useful information and get value from it in making a university more effective. There were some very impressive applications on display, enough to make any university management green with envy. All very good – but the client list is the big beasts of the higher education jungle: University of Michigan (income $3.4bn, 60k students), Oxford University (income £1bn, 22k students), Cornell University (income $3.1bn, 22k students), Brown University (income $700m, 8k students), Texas A&M (income $4.1bn, 53k students), Berkeley (income $2.1bn, 35k students). It can take big bucks to get big data.

In many UK universities budgets are tighter, and investment in the databases and analytics software that frees up big data isn’t this year’s (or next’s) priority. But it isn’t a lost cause: here are seven ideas which can make a real difference.


1. Know what data you have. Universities will have systems to record information and transactions about admissions, enrolments, exams, staff, space, finance, timetabling, learning resources, alumni, donors, research and more. Some of these systems may only be a spreadsheet, or paper-based files stored in one place, but knowing what is there can make a real difference. University management teams will see the possibilities of combining information; planning professionals will want to know what is there, make sure that its meaning is understood, and what the limits are on sharing the data.

2. A focus on data quality can be a real help. Look at where errors are creeping in to your data. Are you double-entering data because systems are not set up to be compatible? Have you got good documentation – with clear, unambiguous and relevant definitions of data fields, and good guidance for users – for all of the IT systems which you use to manage your background processes? A data quality policy will get you a tick from the governing body when it comes to the annual return to the funding council, and it can help you identify where you need to address problems.

3. Use the expertise you have. Universities have plenty of people who understand data and statistics – within the professional services, but also amongst the academic staff. Often these people will be only too pleased to be involved in making the data work better for their university. For staff in a professional services team, being part of a wider group looking at data can be a way to get a glimpse beyond the silo of their current role; and for academic staff, the chance to contribute on an institution-wide basis can be good for career development and professional recognition.  

4. Get in training. Train people in what data you have – sharing this knowledge opens up possibilities.  Train people in using the functionality of spreadsheet software – there’s power in these tools, for analysis and for presentation, which might surprise you. And train people in numerical reasoning – we all know an otherwise-high-performing-professional who has a real block with numbers, and overcoming this can be very empowering for them and for you. 

5. Use the data you have. It’s always possible to want better quality data, in different formats, and bringing together data sets which don’t match. And there are some questions where you do need real accuracy. But the data you have is good enough to help answer an awful lot of questions: focus on what you can say, rather than what you can’t, and don’t let the quest for perfect data get in the way of effective use of data. Read ‘How to measure anything’ by Douglas Hubbard to get a sense of what is possible. And think about letting a postdoc scientist loose on the data – it’s their capacity to see and understand the numbers that matters, not their knowledge of the underlying business. You’ll be surprised at what a data scientist can do!

6. Look for bottlenecks in your systems.  Do you have a colleague whose job it is to manage data requests, or is it a little bit of many people’s jobs?  Is the data team in IT and disconnected with users, making prioritisation difficult?  Sometimes sorting out one or two little problems can have a dramatic effect on how data can be made available and shared.

7. Spring clean your reports. Many data systems have reporting functions which require knowledge of SQL, for instance, to generate a report. Is the library of reports which have been coded a manageable size, and they reports which you still need? Find out what reports have already been written, remove duplicates, specify what you need now, and share the menu with others. Manage requests for new reports – if there’s real value in a new report, then it’s worth coding, but sometimes a colleague can happily use what already exists.


These seven tips won’t give you big data – you’ll still be casting longing glances at the analytics some universities use – but they will help you make an impact. And once the management team gets an appetite for data, who knows where that will go?

Bigger is better?

Every now and then there’s a splash about the sheer number of administrators in higher education – see, for example, Registrarism’s post in February 2014 picking up on a scare story in the Chronicle of Higher Education.  If you set aside the nostalgia for imagined lost days of senior common rooms, pliant students and No Administrators, there is a an interesting question about how much universities actually spend.

In the UK at least this is public data, from HESA.  I looked at the proportion of staff spend by UK universities which was not on academic staff.  I excluded staff spend on premises (ie estates and facilities management) and residences because these are sometimes contracted out, which would skew the data.  And I plotted this data, for 2011-12, against total income of institutions in that same year.  The resulting chart can be found via the Resources page on hughjonesconsulting.co.uk, here.

And what do we see?  Well, there does seem to be a correlation between scale and less spend on professional service staff.  (Remember – correlation does not imply causality, although as Edward Tufte observes, it sure is a hint.)  But what a variation there is too – spend is pretty much all over the place.

It’s important not to jump to conclusions about this.  Importantly, there’s no data here about the quality of the service provided, and maybe you get more and better if you spend more.  And UK universities aren’t all the same, and don’t operate in a vacuum.  So, I’d want to look at subject mix; location; research-intensiveness; and history (because patterns of spend tend to lock themselves in over the years; and because many universities saw their unit of resources squeezed by late 1980’s and early 1990’s public funding mechanisms).

But there’s also food for thought.  Are you above the line or below it?